Shadow effect weakening based on intrinsic image extraction with effective projection of logarithmic domain for road scene

The accuracy of road region extraction is an important factor for the safety and reliability of intelligent driving system. Due to the shadow greatly affects the result of road region extraction in practical applications, the shadow effect weakening on a single image by the optical principles and theories is meaningful. To weaken the shadow effect, we extract the intrinsic images of road scenes based on logarithm domain projection. The intrinsic image has an advantage to remove the shadow effect. Thus, we propose effective projection angle calculation methods in logarithmic domain based on simple statistic, which can eliminate the impact of the direction of camera features. Furthermore, the best whole time projection angle with practical application can be obtained for rapid acquisition of intrinsic image. In the experiment, the proposed methods can weaken the shadow effect for road scene images. In order to evaluate intrinsic image extracted by the proposed methods, the same road detection method is implemented to extract road region. The results demonstrate that the road region detection accuracy based on intrinsic image extracted by our methods are better than the compared methods.

[1]  Yong Li,et al.  Fast vanishing point detection method based on road border region estimation , 2018, IET Image Process..

[2]  Yong Li,et al.  Road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes , 2016, Robotics Auton. Syst..

[3]  Jian Yang,et al.  A Shadow Removal Method for High Resolution Remote Sensing Image , 2008 .

[4]  Chen Yu,et al.  Shadow Removal of Vehicles in a Video System Based on RGB Chroma Model , 2008, 2008 International Conference on Computer Science and Software Engineering.

[5]  J. Cohen,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulas , 1968 .

[6]  Yong Li,et al.  Road extraction algorithm based on intrinsic image and vanishing point for unstructured road image , 2018, Robotics Auton. Syst..

[7]  Victor J. D. Tsai,et al.  A comparative study on shadow compensation of color aerial images in invariant color models , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Alexei A. Efros,et al.  Detecting Ground Shadows in Outdoor Consumer Photographs , 2010, ECCV.

[9]  Vincent Frémont,et al.  Fast road detection from color images , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[10]  Derek Hoiem,et al.  Paired Regions for Shadow Detection and Removal , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Willy Wien,et al.  XXX. On the division of energy in the emission-spectrum of a black body , 1897 .

[12]  Jagdish Mehra,et al.  The Historical Development of Quantum Theory , 1982 .

[13]  Cheng Lu,et al.  Intrinsic Images by Entropy Minimization , 2004, ECCV.

[14]  Yong Li,et al.  Road detection based on illuminant invariance and quadratic estimation , 2019, Optik.

[15]  Hao Jiang,et al.  Shadow resistant tracking using inertia constraints , 2007, Pattern Recognit..

[16]  Antonio M. López,et al.  Road Detection Based on Illuminant Invariance , 2011, IEEE Transactions on Intelligent Transportation Systems.

[17]  Jiandong Tian,et al.  Pixel-wise Orthogonal Decomposition for Color Illumination Invariant and Shadow-free Image , 2014, Optics express.

[18]  Jiandong Tian,et al.  New spectrum ratio properties and features for shadow detection , 2016, Pattern Recognit..